Jellyfish Wants You to Use Large Language Models to Plan Your Ad Buys

Why It Matters

By converting AI‑generated brand sentiment into actionable ad signals, marketers can dramatically increase campaign ROI, accelerating the adoption of generative AI in performance advertising.

Key Takeaways

• Jellyfish's Share of Model quantifies brand mentions across AI outputs
• PMI saw 20% sales lift and 45% conversion rise
• Return on ad spend jumped 156% during 90‑day test
• Performance Max campaigns gain targeting signals from LLM‑derived insights

Pulse Analysis

The advertising landscape is evolving as agencies move beyond using large language models (LLMs) as simple research tools. Jellyfish’s “Share of Model” product captures the frequency and context of brand mentions across AI platforms such as ChatGPT and Gemini, then translates that data into granular targeting parameters for Google’s Performance Max (PMax) campaigns. This methodology bridges the gap between conversational AI insights and programmatic media buying, offering a scalable way to align ad spend with real‑time cultural relevance.

When the Project Management Institute (PMI) piloted the service, the outcomes were striking: a 20% increase in sales volume, a 45% surge in conversions, and a 156% improvement in return on ad spend within just three months. These metrics underscore how AI‑derived audience signals can sharpen bidding strategies, reduce waste, and amplify conversion pathways. For advertisers, the ability to feed LLM‑generated sentiment and thematic data directly into PMax’s automated optimization engine translates into measurable financial upside without the need for extensive manual audience segmentation.

The broader implication for the ad tech ecosystem is a validation of AI‑first media planning. As brands seek to stay ahead of rapidly shifting consumer conversations, tools that convert LLM outputs into actionable media directives will become a competitive differentiator. Jellyfish’s success with PMI suggests that integrating generative AI into the media mix can unlock higher efficiency, especially for performance‑driven channels. Expect more agencies to adopt similar AI‑enhanced workflows, prompting platforms like Google to further refine APIs that ingest external AI signals, ultimately reshaping how budgets are allocated across the digital advertising stack.